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MediaTUM
Article . 2019
Data sources: MediaTUM
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
MediaTUM
Article . 2019
Data sources: MediaTUM
Journal of Energy Resources Technology
Article . 2019 . Peer-reviewed
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Numerical Approaches for Modeling Gas–Solid Fluidized Bed Reactors: Comparison of Models and Application to Different Technical Problems

Authors: Annelies Vandersickel; Hartmut Spliethoff; Stephan Gleis; P. Ostermeier;

Numerical Approaches for Modeling Gas–Solid Fluidized Bed Reactors: Comparison of Models and Application to Different Technical Problems

Abstract

Gas–solid fluidized bed reactors play an important role in many industrial applications. Nevertheless, there is a lack of knowledge of the processes occurring inside the bed, which impedes proper design and upscaling. In this work, numerical approaches in the Eulerian and the Lagrangian framework are compared and applied in order to investigate internal fluidized bed phenomena. The considered system uses steam/air/nitrogen as fluidization gas, entering the three-dimensional geometry through a Tuyere nozzle distributor, and calcium oxide/corundum/calcium carbonate as solid bed material. In the two-fluid model (TFM) and the multifluid model (MFM), both gas and powder are modeled as Eulerian phases. The size distribution of the particles is approximated by one or more granular phases with corresponding mean diameters and a sphericity factor accounting for their nonspherical shape. The solid–solid and fluid–solid interactions are considered by incorporating the kinetic theory of granular flow (KTGF) and a drag model, which is modified by the aforementioned sphericity factor. The dense discrete phase model (DDPM) can be interpreted as a hybrid model, where the interactions are also modeled using the KTGF; however, the particles are clustered to parcels and tracked in a Lagrangian way, resulting in a more accurate and computational affordable resolution of the size distribution. In the computational fluid dynamics–discrete element method (CFD–DEM) approach, particle collisions are calculated using the DEM. Thereby, more detailed interparticulate phenomena (e.g., cohesion) can be assessed. The three approaches (TFM, DDPM, CFD–DEM) are evaluated in terms of grid- and time-independency as well as computational demand. The TFM and CFD–DEM models show qualitative accordance and are therefore applied for further investigations. The MFM (as a variation of the TFM) is applied in order to simulate hydrodynamics and heat transfer to immersed objects in a small-scale experimental test rig because the MFM can handle the required small computational cells. Corundum is used as a nearly monodisperse powder, being more suitable for Eulerian models, and air is used as fluidization gas. Simulation results are compared to experimental data in order to validate the approach. The CFD–DEM model is applied in order to predict mixing behavior and cohesion effects of a polydisperse calcium carbonate powder in a larger scale energy storage reactor.

Keywords

fluidized bed; computational fluid dynamics; model comparison; hydrodynamics; heat transfer, ddc: ddc:620, ddc: ddc:

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    Top 10%
    influence
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
Green